https://github.com/yaofang-liu/fvdm
공식 코드Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach
Papers with Code 아카이브
Video Generation
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<span style="color:grey; opacity: 0.6">( Various Video Generation Tasks. Gif credit: MaGViT )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yaziwel/feat
공식 코드FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation
https://github.com/ybybzhang/controlvideo
공식 코드ControlVideo: Training-free Controllable Text-to-Video Generation
https://github.com/ybybzhang/framepainter
공식 코드FramePainter: Endowing Interactive Image Editing with Video Diffusion Priors
https://github.com/ybybzhang/videoelevator
공식 코드VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion Models
https://github.com/yhZhai/idol
공식 코드IDOL: Unified Dual-Modal Latent Diffusion for Human-Centric Joint Video-Depth Generation
https://github.com/yikaiw/vidu4d
공식 코드Video4DGen: Enhancing Video and 4D Generation through Mutual Optimization
https://github.com/yingqinghe/lvdm
공식 코드Latent Video Diffusion Models for High-Fidelity Long Video Generation
https://github.com/yiranran/Audio-driven-TalkingFace-HeadPose
공식 코드Audio-driven Talking Face Video Generation with Learning-based Personalized Head Pose
https://github.com/yisuitt/mobius
공식 코드Mobius: Text to Seamless Looping Video Generation via Latent Shift
https://github.com/yitong91/StoryGAN
공식 코드StoryGAN: A Sequential Conditional GAN for Story Visualization
https://github.com/youncy-hu/mage
공식 코드Make It Move: Controllable Image-to-Video Generation with Text Descriptions
https://github.com/youngfly/Mobius
공식 코드https://github.com/yuanhy1997/seqdiffuseq
공식 코드SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers
https://github.com/yumingj/text2performer
공식 코드https://github.com/yvanyin/drivingworld
공식 코드DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT
https://github.com/zehong-ma/magcache
공식 코드https://github.com/zgctroy/realcam-vid
공식 코드RealCam-Vid: High-resolution Video Dataset with Dynamic Scenes and Metric-scale Camera Movements
https://github.com/zhangzjn/DTVNet
공식 코드DTVNet: Dynamic Time-lapse Video Generation via Single Still Image
https://github.com/zhen-dong/magic-me
공식 코드https://github.com/zheng-chong/catv2ton
공식 코드CatV2TON: Taming Diffusion Transformers for Vision-Based Virtual Try-On with Temporal Concatenation
https://github.com/zhengtongxu/vilp
공식 코드https://github.com/zhenzhiwang/humanvid
공식 코드HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation
https://github.com/zhuangshaobin/vlogger
공식 코드https://github.com/zibojia/COCOCO
공식 코드https://github.com/zli12321/awesome-vlm-papers-and-models
공식 코드https://github.com/zli12321/videohallu
공식 코드VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video Understanding
https://github.com/zli12321/vision-language-models-overview
공식 코드https://github.com/zshyang/amg
공식 코드https://github.com/zt220501/stork
공식 코드STORK: Improving the Fidelity of Mid-NFE Sampling for Diffusion and Flow Matching Models
https://github.com/zyxelsa/motioncrafter
공식 코드MotionCrafter: One-Shot Motion Customization of Diffusion Models
https://github.com/2023-MindSpore-1/ms-code-192
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/gan
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/2023-MindSpore-1/ms-code-23
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample
https://github.com/2023-MindSpore-1/ms-code-65
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/360CVGroup/FancyVideo
FancyVideo: Towards Dynamic and Consistent Video Generation via Cross-frame Textual Guidance
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.